Quality control on the frontier

Area of Science:

• Genomics
• Transcriptomics
• Bioinformatics

Background:

• High-throughput sequencing generates vast datasets, posing significant data quality control challenges.
• A universal set of quality metrics is insufficient for all sequencing conditions and library types.
• Effective quality control is crucial for reliable downstream analysis in genomics and transcriptomics.

Purpose of the Study:

• To present open-source software solutions for comprehensive data quality control in high-throughput sequencing.
• To provide both general and specific quality metrics tailored for Illumina sequencing platforms.
• To support the Exeter Sequencing Service in its quality control workflows.

Main Methods:

• Utilized a suite of open-source bioinformatics tools.
• Implemented quality control pipelines for genomic libraries.
• Developed specific metrics for transcriptomic libraries on Illumina platforms.

Main Results:

• Demonstrated the utility of selected open-source software for quality assessment.
• Provided a framework for generic quality control applicable across various library types.
• Highlighted platform-specific metrics for Illumina-based genomic and transcriptomic data.

Conclusions:

• Open-source software offers flexible and powerful solutions for high-throughput sequencing data quality control.
• A combination of generic and specific metrics ensures robust quality assessment for diverse library types.
• The presented methods enhance the reliability of genomic and transcriptomic data generated on Illumina platforms.